Essays on behavioral economics with information orientation.

• Other Authors: Hu, Hao
• Format: Others
• Language: English; Chinese
• Published: 2013
• Subjects: Economics > Psychological aspects; Social capital (Sociology); Social learning; Social networks
• Online Access: http://library.cuhk.edu.hk/record=b5549739; http://repository.lib.cuhk.edu.hk/en/item/cuhk-328619

行为经济学试图分析决策过程对经济行为和结果的影响。我的博士论文由四篇独立的论文组成，运用经济学和心理学的理论视角，研究了个人和群体的决策和行为模式。本论文探讨的问题包括建立社会关系的过程中交互性的影响、队列中的信息外部性、群体决策中较差决定的概率支配、引领型消费者和追随型消费者的不同行为模式等。 === 在题为“社会资本与电信客流失：移动网络中交互性“的第一篇论文中， 我实证检验交互性在发展社会关系中的作用。基于包括网络连接度、社会关系强度、交互性等社会资本要素，我设计了一种预测电信客流失的方法。该算法基于源自用电信服务数据的社会资本度量和服务状态，因此它能较为容易的运用于现实数据库和客关系管理。 === 在题为“一种顺序行为中的信息积累模型“的第二篇论文中，我研究了顺序模式的社会学习过程，提出了一种基于多阶段决策过程的度量信息积累程度和评介信息瀑布稳定性的数学模型。理论结果反映了信息瀑布中行为模式的两个主要特征：信息积累和边际效应递减。 === 在题为“社会学习和群体的智慧：网络中的同步行为“的第三篇论文中，我分析了个人行为通过学习策略在网络中蔓延的现象。这种基于网络的同步学习是通过仿真和数值实验得到了直观的。本研究证明，网络中的同步学习对个体和群体表现有促进作用。同时，其促进的程度是存在阈值的。 === 在题为“社会化媒体中的学习：社会化线索与决策偏差“的第四篇论文中, 我通过分析网络爬虫收集的互联网数据，探讨了社会化媒体中学习策略的影响。这项研究表明，流行性产品的选择中起主导作用的是社会化线索。社会化线索放大了产品之间的销量的差异。此外，我还发现了社会化线索的两种不对称性：（1）社会学习过程中人们对长期变化和短期变化的非对称敏感性；（2）领导型用和追随型用提供好坏口碑的非对称性。这些发现表明，社会线索在决策过程中是存在偏差的。 === Behavioral economics tries to understand the impact of decision process on economic behaviors and outcomes. Utilizing theoretical lenses of psychology and economics, my dissertation, composed of four essays, studies the behavioral pattern of individuals and groups to tell philosophies behind some interesting phenomena, such as reciprocity in developing social relations, information externality in queues, probable dominance of collectively bad decisions, the contradicting behavioral pattern of leaders and followers when facing bad choices, and etc. === In my first essay entitled “Social Capital and Telecom Churn: Reciprocity in Mobile Telecom Networks“, I empirically examine the role of reciprocity in the development of social relations. Based on multiple dimensions of social capital, e.g., network connectivity, social tie strength, internal network ratio, and reciprocity, I develop a method to predict telecom churns. The algorithm is based on social capital derived from historical usage patterns and service status, thus it is easy to be implemented with customer database. === In my second essay entitled “A Model of Information Aggregation in Sequential Moves“, I investigate sequential learning process and propose a mathematical model that measure information aggregation and evaluate the stability of informational cascades with a multi-stage decision process. The results capture two primary behavioral aspect of informational cascade: information aggregation and diminishing sensitivity. === In my third essay entitled “Social Learning and the Wisdom of Crowd: Simultaneous Moves in Network“, I investigate the phenomenon of social contagion through learning strategies among individuals in the network. This network-based simultaneous learning process is simulated via computer programme to seek insights on the effect of simultaneous learning on collective and individual actions. Through numerical experiment, it demonstrats that learning in network can be effective while bad decisions have chance to dominate, and there is a threshold for collective decision quality. === In my forth essay entitled “Learning in Social Media: Social Cues and Decision Biases“, I investigate the effect of learning strategy in the context of social media with internet usage dataset collected by web crawler. This study demonstrates that choices based on social cues dominate for popular products, and it exaggerates the inequality among products. Besides, two types of asymmetries exist for social cues: (1) Asymmetric sensitivity for immediate and accumulative changes in social learning process; and (2) Asymmetric behavioral pattern in providing WOMs for leaders and followers. These findings suggest that social cues may be biased. === Essay 1. Social capital and telecom churn: reciprocity in mobile telecom networks -- essay 2. A cognitive model of information aggregation in sequential moves -- essay 3. Social learning and the wisdom of crowd: simultaneous moves in network -- essay 4. Learning in social media: social cues and decision biases. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Detailed summary in vernacular field only. === Hu, Hao. === Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. === Includes bibliographical references. === Abstracts also in Chinese. === PREFACE --- p.1 === SOCIAL CAPITAL AND TELECOM CHURN: RECIPROCITY IN MOBILE TELECOM NETWORKS --- p.6 === Chapter 1. --- INTRODUCTION --- p.7 === Chapter 2. --- LITERATURE REVIEW --- p.7 === Chapter 2.1. --- Service Continuity and Churn --- p.7 === Chapter 2.2. --- Social Capital in the Network --- p.9 === Chapter 3. --- THEORETICAL FRAMEWORK --- p.11 === Chapter 3.1. --- Network Connectivity --- p.12 === Chapter 3.2. --- Social Tie Strength --- p.12 === Chapter 3.3. --- Internal Network Ratio --- p.13 === Chapter 3.4. --- Reciprocal Social Norm --- p.13 === Chapter 3.5. --- Service Continuity and Churn --- p.14 === Chapter 4. --- EMPIRICAL ANALYSIS --- p.14 === Chapter 4.1. --- Research Setting and Data Collection --- p.14 === Chapter 4.2. --- Variables and Proxies --- p.14 === Chapter 4.3. --- Social Capital and Service Continuity --- p.17 === Chapter 4.4. --- Service Continuity and Churn --- p.20 === Chapter 5. --- PREDICTIVE MODEL FOR TELECOM CHURN --- p.21 === Chapter 5.1. --- Performance Assessment Criteria --- p.21 === Chapter 5.2. --- Bench Mark Model --- p.21 === Chapter 5.3. --- Three-Stage Model --- p.23 === Chapter 6. --- DISCUSSION AND CONCLUDING REMARKS --- p.25 === Chapter 6.1. --- Theoretical Extension --- p.25 === Chapter 6.2. --- Managerial Implication --- p.26 === Chapter APPENDIX I --- MEASUREMENT CLASSIFICATION --- p.27 === Chapter APPENDIX II --- DATA DESCRIPTION --- p.28 === REFERENCES --- p.30 === A COGNITIVE MODEL OF INFORMATION AGGREGATION IN SEQUENTIAL MOVES --- p.32 === Chapter 1. --- INTRODUCTION --- p.33 === Chapter 2. --- THEORETICAL FRAMEWORK --- p.34 === Chapter 2.1. --- Conceptual Background --- p.34 === Chapter 2.2. --- Decision Scenarios --- p.35 === Chapter 2.3. --- Hypothesis --- p.38 === Chapter 3. --- ANALYTICAL MODEL --- p.40 === Chapter 3.1. --- Model Setup --- p.40 === Chapter 3.2. --- Sequential Analysis --- p.41 === Chapter 4. --- NUMERICAL ANALYSIS --- p.43 === Chapter 4.1. --- Margin Analysis --- p.43 === Chapter 4.2. --- Product Complexity --- p.44 === Chapter 4.3. --- Information Revealing --- p.46 === Chapter 5. --- CONCLUSION --- p.47 === Chapter References --- p.48 === SOCIAL LEARNING AND THE WISDOM OF CROWD: SIMULTANEOUS MOVES IN NETWORK --- p.50 === Chapter 1. --- INTRODUCTION --- p.51 === Chapter 2. --- THE PARADIGM OF A NESTED WORLD --- p.52 === Chapter 2.1. --- Bounded Rationality and Social Learning --- p.53 === Chapter 2.2. --- Social Learning and Conformity tendency --- p.54 === Chapter 2.3. --- Summary - Judgment and Collective Behavior --- p.55 === Chapter 3. --- THEORETICAL FRAMEWORK --- p.56 === Chapter 3.1. --- Primary Investigation --- p.57 === Chapter 3.2. --- Secondary Investigation --- p.61 === Chapter 4. --- RESEARCH METHODOLOGY --- p.62 === Chapter 4.1. --- Construct Measures --- p.62 === Chapter 4.2. --- Computational Model --- p.64 === Chapter 4.3. --- Numerical Experiment --- p.67 === Chapter 4.4. --- Pattern Analysis --- p.68 === Chapter 5. --- CONCLUSION --- p.73 === Chapter 6. --- IMPLICATION --- p.74 === REFERENCES --- p.75 === LEARNING IN SOCIAL MEDIA: SOCIAL CUES AND DECISION BIASES --- p.78 === Chapter 1. --- INTRODUCTION --- p.79 === Chapter 2. --- RESEARCH BACKGROUNDS --- p.81 === Chapter 2.1. --- Social Cues and Choices --- p.81 === Chapter 2.2. --- Decision Biases of Social Cues --- p.82 === Chapter 3. --- HYPOTHESES --- p.83 === Chapter 3.1. --- Comparative Impact of Social Cues --- p.84 === Chapter 3.2. --- Reference Dependence in Social Learning --- p.85 === Chapter 3.3. --- WOM Driven by Leaders Confirmatory Bias and Followers Regret --- p.86 === Chapter 4. --- RESEARCH METHODOLOGY --- p.87 === Chapter 4.1. --- Data Summary --- p.88 === Chapter 4.2. --- Empirical Analysis --- p.89 === Chapter 5. --- DISCUSSION AND CONCLUDING REMARKS --- p.94 === REFERENCES --- p.95